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VEDesc: vertex-edge constraint on local learned descriptors.

Jianhua Yin1, Longzhen Zhu1, Yang Bai1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Shenzhen, China.

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This study introduces a novel vertex-edge constraint (VEC) triplet loss to enhance local learned descriptors by addressing patch descriptor inconsistency. The method improves descriptor spatial distribution and achieves competitive performance on various datasets.

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Local learned descriptors are crucial for computer vision tasks.
  • Triplet loss networks achieve state-of-the-art performance but face descriptor inconsistency.
  • This inconsistency leads to irregular spatial distribution of descriptors.

Purpose of the Study:

  • To propose a novel method to overcome the inconsistency problem in local learned descriptors.
  • To improve the spatial distribution and reliability of local descriptors.

Main Methods:

  • Designed a vertex-edge constraint (VEC) triplet loss function.
  • Incorporated the correlation between two descriptors within a patch into the loss function.
  • Developed an exponential algorithm to minimize the influence of non-matching descriptors by reducing side differences.

Main Results:

  • The proposed VEC triplet loss effectively addresses descriptor inconsistency.
  • The method demonstrates competitive performance compared to state-of-the-art techniques.
  • Improved spatial distribution of local learned descriptors was observed.

Conclusions:

  • The vertex-edge constraint (VEC) triplet loss is an effective approach for improving local learned descriptors.
  • The proposed method offers a significant advancement in descriptor reliability and spatial consistency.
  • This work contributes to more robust feature matching in computer vision.